A multiple model cost-sensitive approach for intrusion detection

Abstract

Intrusion detection systems (IDSs) need to maximize security while minimizing costs. In this paper, we study the problem of building cost-sensitive intrusion detection models to be used for realtime detection. We briefly discuss the major cost factors in IDS, including consequential and operational costs. We propose a multiple model cost-sensitive machine learning technique to produce models that are optimized for user-defined cost metrics. Empirical experiments in offline analysis showa reduction of approximately 97% in operational cost over a single model approach, and a reduction of approximately 30% in consequential cost over a pure accuracy-based approach.